📱Machine Learning and Artificial intelligence
📱Hands-On Data Annotation: Applied Machine Learning

🔅 Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions

🌐 Author: Keith McCormick
🔰 Level: Intermediate

⏰ Duration: 2h 9m

🌀 Learn best practices for how to produce explainable AI and interpretable machine learning solutions.

📗 Topics: Machine Learning, Artificial Intelligence

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📱Machine Learning and Artificial intelligence
📱Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions

🔅 AI Pair Programming with GitHub Copilot X

🌐 Author: Ronnie Sheer
🔰 Level: Advanced

⏰ Duration: 1h 23m

🌀 Learn how to streamline software development workflows using AI pair programming with GitHub Copilot X.

📗 Topics: Pair Programming, GitHub Copilot, Artificial Intelligence

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📱Machine Learning and Artificial intelligence
📱AI Pair Programming with GitHub Copilot X

🔅 Machine Learning and AI Foundations: Causal Inference and Modeling

🌐 Author: Keith McCormick
🔰 Level: Advanced

⏰ Duration: 2h 51m

🌀 Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and how to use them.

📗 Topics: Causal Inference, Machine Learning, Artificial Intelligence

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📱Machine Learning and Artificial intelligence
📱Machine Learning and AI Foundations: Causal Inference and Modeling

🔅 Building a Video Transcriber with Node.js and Google AI Speech-To-Text API

🌐 Author: Fikayo Adepoju
🔰 Level: Intermediate

⏰ Duration: 1h 9m

🌀 Learn how to transcribe audio from video by integrating Node.js applications with the Google AI Speech-to-Text API.

📗 Topics: Machine Trannoscription, Artificial Intelligence, Node.js

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📱Machine Learning and Artificial intelligence
📱Building a Video Transcriber with Node.js and Google AI Speech-To-Text API

💸 Understanding Popular ML Algorithms:

1️⃣ Linear Regression: Think of it as drawing a straight line through data points to predict future outcomes.

2️⃣ Logistic Regression: Like a yes/no machine - it predicts the likelihood of something happening or not.

3️⃣ Decision Trees: Imagine making decisions by answering yes/no questions, leading to a conclusion.

4️⃣ Random Forest: It's like a group of decision trees working together, making more accurate predictions.

5️⃣ Support Vector Machines (SVM): Visualize drawing lines to separate different types of things, like cats and dogs.

6️⃣ K-Nearest Neighbors (KNN): Friends sticking together - if most of your friends like something, chances are you'll like it too!

7️⃣ Neural Networks: Inspired by the brain, they learn patterns from examples - perfect for recognizing faces or understanding speech.

8️⃣ K-Means Clustering: Imagine sorting your socks by color without knowing how many colors there are - it groups similar things.

9️⃣ Principal Component Analysis (PCA): Simplifies complex data by focusing on what's important, like summarizing a long story with just a few key points.